R

• An open source language developed for statistical computing by Ross Ihaka and Robert Gentleman
• Inspired by the S language developed at Bell labs in 1976 to make interactive data analysis easier
• The first official version was released in 2000

Why use R for data analysis?

• R is and will always be free
• Can easily implement any statistical analysis
• Code serves as a record which enables reproducibility with minimal effort
• As of March 2023, there were over 19,000 open source packages to extend its functionality
  • Highly customizable graphics (ggplot2)
  • Analysis reports (knitr)
  • RNA-seq analysis (DESeq2)

How does it work?

RStudio

• RStudio is an integrated development environment (IDE)
  
• It is an app that makes R code easier to write by providing a feature rich graphical user interface (GUI)

Layout

File types

• Rscript files that end in .R
  • The most basic, a file that contains R code
• RMarkdown files that end in .Rmd
• Let’s create a blank Rscript to see how they work, open RStudio and click:
  • File -> New File -> R Script

R Markdown

• A file format combining R code with Markdown for text formatting.
• Designed for creating reproducible research reports in various formats (HTML, PDF, Word).
• Let’s create an Rmd file in RStudio to explore the basics of how they work:
• File -> New File -> R Markdown

R Markdown Advanced Usage

• Presentations: Creating slides (like these) with revealjs.
• Publications: Authoring online books that combine narrative, code, and output with bookdown.
• Interactive Documents: Developing interactive tutorials or dashboards with learnR and other embedded applications.

Variable definition

• Variables store information that is referenced and manipulated in a computer program
• There are 3 ways to define variables in R, but one is preferred:

x <- 1  # Preferred way
x = 1
1 -> x
print(x)

[1] 1

Example

• Run the following in the R console:

x <- 1  
y <- 4
z <- y
x + y + z

[1] 9

Errors

• Errors: Stop the execution of your code and must be fixed for the code to run successfully

x <- 5
y <- 10
z <- x + a

Error: object 'a' not found

Common Errors

• Syntax Error: Invalid R code syntax (e.g. misplaced parentheses)

Error: unexpected ")"

• Object not found: This variable is not defined (e.g. misspelled variables)

Error: object "a" not found

See this article for more common errors and how to fix them.

Warnings

• Do not stop the execution but indicate potential issues that you should be aware of and might need to address

a <- c(1, 2, 3, 4, 5)
b <- c(6, 7, 8, 9)
result <- a + b

Warning in a + b: longer object length is not a multiple of shorter object
length

Variable Naming

• Variables names must start with a letter and can contain underscores and periods
• It is best practice to use descriptive variable names and stick to one style of names

# Snake case
dog_breeds <- c("Labrador Retriever", "Akita", "Bulldog")

# Period separated
dog.breeds <- c("Labrador Retriever", "Akita", "Bulldog")

# Camel case
DogBreeds <- c("Labrador Retriever", "Akita", "Bulldog")

Poll 2

Which variable name is not valid in R?

1. cat_dog
2. CatDOG
3. cat.dog
4. catD*g

Excercise 1

• Open Rscript file part_1.R in Rstudio

Data Types

• Integer
  • Whole numbers (in R denoted with L ex. 1L,2L)
• Numeric
  • Decimal numbers
• Logical
  • Boolean (TRUE, FALSE)
  • NA (missing data)
• Character
  • Letters and strings of letters
  • “A”, “Labrador Retriever”

Poll 3

Which of these is not the correct data type for the value?

1. 1.5 - Numeric
2. “1” - Character
3. NA - Logical
4. 1 - Integer

Data Structures

• Vectors
  • Atomic vectors - one dimensional lists that store values of the same type
  • Lists - can be multidimensional and contain different types/structures (ex. nested lists)
• Factors
  • Ordered list with assigned levels
• Matrix
  • Columns and rows of the same type
• Data frames
  • Columns and rows of mixed types

Exercise 2: Data Types and Structures

• Reopen Rscript file part_1.R in Rstudio

Math & Logic

• Built in functions to get common mathematical summaries of data (eg. mean( ), median( ), mode( ) )
• Relational comparison operators to compare values

x == y  # Equal to
x != y  # Not equal to
x <  y  # Less than
x > y   # Greater than
x <= y  # Less than or equal to
x >= y  # Greater than or equal to

x %in% y # Is x in this vector y?

Poll 4

What is the output of the following code?

4 %in% as.character(c(1,2,3,4))

1. TRUE
2. FALSE
3. NA

Logical Operators

• Logical operators can compare TRUE or FALSE values

x <- TRUE
y <- FALSE

!x     # Not x
x | y  # x or y
x & y  # x and y

Poll 5

What is the output of the following code?

x <- TRUE
y <- FALSE

x & !y

1. TRUE
2. FALSE
3. NA

Conditional execution

• Relational and logical operations allow for conditional execution of code

dog_breeds <- c("Labrador Retriever", "Akita", "Bulldog")
if ("Akita" %in% dog_breeds) {
  print("dog_breeds already contains Akita")
} else {
  dog_breeds <- c("Akita", dog_breeds)
}

[1] "dog_breeds already contains Akita"

Functions

• A function is block of organized, reusable code that is used to perform a single action
• R has many built in functions, these are called base R functions
• Not all arguments are required and some have default values

Defining a function

• To define a function we use the function keyword, the output is specified with the return keyword:

add_dog <- function(dog_to_add,
                    input_vector) {
  if (dog_to_add %in% input_vector) {
    
    message("Already contains this dog")
    
  } else {
    
    output <- c(dog_to_add, input_vector)
    return(output)
    
  }
}

Example

add_dog(dog_to_add = "Akita",
        input_vector = dog_breeds)

Already contains this dog

add_dog(dog_to_add = "German Shepard",
        input_vector = dog_breeds)

[1] "German Shepard"     "Labrador Retriever" "Akita"             
[4] "Bulldog"           

Poll 6

What does this function do?

mystery_function <- function(x) {
  if (x > 0) {
    return(x)
  } else {
    return(-x)
  }
}

1. Returns the absolute value of x
2. Returns x
3. Returns the square root of x
4. Returns -x

Packages

• Packages are collections of functions that are specialized to a specific task (plotting, data manipulation etc.)

library(ggplot2) # Makes all of the ggplot2 functions available

• The tidyverse is a collection of commonly used data analysis packages
  • Learning curve is less steep
  • Lots of useful packages for cleaning and “wrangling” data into the correct format

Why use Tidyverse Packages?

• Most of the work in data analysis is getting data into the correct format to create outputs
• The tidyverse collection of packages simplifies this process
  • Intuitive syntax
  • Comprehensive (data manipulation, cleaning, modeling and graphics)
  • Consistent data structure
  • Strong community support

Workshop survey

• Please fill out our workshop survey so we can continue to improve these workshops

Upcoming Workshops

Single Cell ATAC-Seq Data Analysis Part 2

• Check this link at the end of the summer for out fall workshop schedule